Bayesian hybrid Kalman filter auto-regressive for smarter electricity load forecasting

Energy management efficiency requires highly accurate electricity load forecasting, especially in dynamic and complex environments. This study presents the Bayesian Hybrid Kalman Filter Auto-Regressive (BAR-KF) model as an advanced technique for improving load forecasting accuracy. This hybrid framework addresses the fundamental limitations of the autoregressive model and the Kalman filter model in previous works by better handling non-stationarity and model uncertainty through Markov Chain Monte Carlo (MCMC) methods in estimating the posterior of AR parameters, which are subsequently integrated into the Kalman filter framework. The analysis of hourly electricity consumption data demonstrates the model's ability to capture temporal dependencies and provide probabilistic forecasts that offer a better understanding of possible load ranges. The results offer valuable insights into the dynamics of electricity consumption, aiding policymakers and grid experts in building greater operational resilience, distributing load more effectively, and consequently improving forecast accuracy. The interaction between the MCMC-derived model parameters and their adaptive mechanisms enhances the robustness of the Kalman filter, making the forecast model more responsive to innovative approaches for electricity demand.